liveBook · Manning

Error handling

As explained in Starting Data Analytics with Generative AI and Python

A technique for detecting, responding to, and recovering from software or service failures.

When a generative AI request fails or takes too long, error handling helps keep the integration stable. Use retries to respond to timeouts and rate-limit errors. section 1.3.6

Retries can improve reliability, but they do not guarantee that every request will succeed. Check that the integration behaves as expected when errors occur. section 1.3.6

How Starting Data Analytics with Generative AI and Python covers it

How deep it goes
Recommends retries for timeouts and rate limits but gives no implementation steps.
Examples
Names timeouts and rate-limit errors as cases for retry handling.
What you can do after
Apply retry mechanisms to handle request timeouts and rate-limit errors.
How it connects
Links retries with timeouts and rate limiting.

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This explainer was generated by Manning's knowledge graph from the text of Starting Data Analytics with Generative AI and Python. The section links point to the parts of the book it draws on.